Life-Cycle Greenhouse Gas Emissions of Transportation Fuels: Issues and Implications for Unconventional Fuel Sources
Notice bibliographique
Résumé
Abstract The effectiveness and efficiency of regulatory and other policy approaches intended to reduce the greenhouse gas emissions from transportation fuels can hinge on the fuel life-cycle analysis (LCA). Emerging regulation has raised urgent questions about both definition and evaluation of life-cycle emissions, and the effectiveness, efficiency and equity of regulatory approaches which use such analyses. This paper focuses on the LCA for transportation fuels from unconventional hydrocarbon sources and associated regulatory issues and implications, and examines these in the context of experience gained in the study of conventional hydrocarbon sources, biofuels, electric vehicles, and other alternatives. Critical issues arise in the regulatory use of life-cycle emissions analysis when comparing different types of fuels, for different types of vehicles, including:Uncertainty in life-cycle emissions - Differences in estimates of the life-cycle emissions for one fuel can exceed the differences in estimates for different fuels; boundaries, accounting, aggregation and accuracy of LCA are each critical and determining issues in its application in regulations.Flexible pathways - In order to incentivize innovation in fuel production, many pathways (with the ability to be altered) are needed to map production from each individual agent, who will each have their own process.Energy security - Regulation to lower the life-cycle emissions is often also intended to improve energy security (e.g. by increasing supplies of indigenous biofuels); however, in the case of unconventional sources of oil such regulations may aggravate energy security. For complex policies, such as those involving LCA - especially where there are international ramifications - much broader dialogue is needed to improve the policy's effectiveness, efficiency and ultimately credibility. INTRODUCTION Emerging regulation of life-cycle greenhouse gas emissions for transportation fuels has raised urgent questions about both definition and evaluation of life cycle emissions, and the effectiveness, efficiency and equity of regulatory approaches which use such analyses. The net emissions from a transportation fuel system depend on the definition of system boundaries, which should be appropriate for its use whether that be to provide insight or for a specific regulatory application. For example, inclusion of emissions from the production of vehicles would add to the system--or life-cycle - greenhouse gas emissions of a transportation fuel. For petroleum-based transportation fuels, the use of the fuel results in about five times the greenhouse gas emissions as in its production (EU JRC 2011, NETL 2008). Consistent application of aggregation, accuracy, and transparency of data are also important when making a comparison between any two production pathways. Critical issues arise in the regulatory use of life-cycle emissions when comparing different types of fuels, for different types of vehicles. It is important to note that a life-cycle assessment tool is not needed for regulatory use if there is a comprehensive policy on emissions across all regions and sectors of society - the cost of emissions would be accounted for where they occur. However, in the absence of a comprehensive policy, accurate and consistent life-cycle assessment can have a useful role when accounting for emissions from the life cycle of a fuel.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».